Details of Research Outputs

TitleProbabilistic forecasting with temporal convolutional neural network
Author (Name in English or Pinyin)
Chen, Y.1; Kang, Y.2; Chen, Y.3; Wang, Z.4
Date Issued2020
Source PublicationNeurocomputing
Indexed BySCOPUS
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
[1] S. Bai, J. Z. Kolter, V. Koltun, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, 1803.01271 (2018).
[2] Bandara, K., Bergmeir, C., Smyl, S., Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. Expert Syst. Appl., 140, 2020, 112896.
[3] F.M. Bianchi, E. Maiorino, M.C. Kampffmeyer, A. Rizzi, R. Jenssen, An Overview and Comparative Analysis of Recurrent Neural Networks for Short Term Load Forecasting, arXiv preprint: 1705.04378 (2017).
[4] A. Borovykh, S. Bohte, C.W. Oosterlee, Conditional Time Series Forecasting With Convolutional Neural Networks, 2017. arXiv preprint: 1703.04691.
[5] Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., Time Series Analysis: Forecasting and Control. 2015, John Wiley & Sons.
[6] T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, Z. Zhang, Mxnet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems, arXiv preprint: 1512.01274 (2015).
[7] Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, 1724–1734.
[8] Gasthaus, J., Benidis, K., Wang, Y., Rangapuram, S.S., Salinas, D., Flunkert, V., Januschowski, T., Probabilistic forecasting with spline quantile function RNNs. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 2019, 1901–1910.
[9] A. Graves, Generating Sequences With Recurrent Neural Networks, arXiv preprint: 1308.0850 (2013).
[10] J. Gu, J. Bradbury, C. Xiong, V.O. Li, R. Socher, Non-Autoregressive Neural Machine Translation, arXiv preprint: 1711.02281 (2017).
[11] He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 770–778.
[12] He, K., Zhang, X., Ren, S., Sun, J., Identity mappings in deep residual networks. Proceedings of the European Conference on Computer Vision, 2016, Springer, 630–645.
[13] Hyndman, R., Koehler, A.B., Ord, J.K., Snyder, R.D., Forecasting with Exponential Smoothing: The State Space Approach. 2008, Springer Science & Business Media.
[14] Hyndman, R.J., Athanasopoulos, G., Forecasting: Principles and Practice. 2018, OTexts.
[15] Ioffe, S., Szegedy, C., Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, 2015, 448–456.
[16] Kaggle, Web Traffic Time Series Forecasting, 2017, (
[17] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y., LightGBM: a highly efficient gradient boosting decision tree. proceedings of the Advances in Neural Information Processing Systems, 2017, 3146–3154.
[18] Koenker, R., Bassett Jr, G., Regression quantiles. Econometrica 46:1 (1978), 33–50.
[19] Laptev, N., Yosinski, J., Li, L.E., Smyl, S., Time-series extreme event forecasting with neural networks at Uber. Proceedings of the International Conference on Machine Learning, 2017.
[20] Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y., Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16:2 (2015), 865–873.
[21] D.C. Maddix, Y. Wang, A. Smola, Deep Factors With Gaussian Processes for Forecasting, arXiv preprint: 1812.00098 (2018).
[22] Makridakis, S., Spiliotis, E., Assimakopoulos, V., The M4 competition: results, findings, conclusion and way forward. Int. J. Forecast. 34:4 (2018), 802–808.
[23] Makridakis, S., Spiliotis, E., Assimakopoulos, V., Statistical and machine learning forecasting methods: concerns and ways forward. PLoS ONE, 13(3), 2018, e0194889.
[24] T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient Estimation of Word Representations in Vector Space, arXiv preprint: 1301.3781 (2013).
[25] Mikolov, T., Karafiát, M., Burget, L., Černockỳ, J., Khudanpur, S., Recurrent neural network based language model. Proceedings of the Eleventh Annual Conference of the International Speech Communication Association, 2010, 1045–1048.
[26] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., Distributed representations of words and phrases and their compositionality. Proceedings of the Advances in Neural Information Processing Systems, 2013, 3111–3119.
[27] Nair, V., Hinton, G.E., Rectified linear units improve restricted Boltzmann machines. Proceedings of the Twenty-Seventh International Conference on Machine Learning, 2010, 807–814.
[28] A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A.W. Senior, K. Kavukcuoglu, Wavenet: A generative model for raw audio, arXiv preprint: 1609.03499 (2016).
[29] Pankratz, A., Forecasting with Dynamic Regression Models. 935, 2012, John Wiley & Sons.
[30] Pascanu, R., Mikolov, T., Bengio, Y., On the difficulty of training recurrent neural networks. Proceedings of the International Conference on Machine Learning, 2013, 1310–1318.
[31] Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T., Deep state space models for time series forecasting. Proceedings of the Advances in Neural Information Processing Systems, 2018, 7795–7804.
[32] Sagheer, A., Kotb, M., Time series forecasting of petroleum production using deep lstm recurrent networks. Neurocomputing 323 (2019), 203–213.
[33] Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T., DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast., 2019 Forthcoming.
[34] Shen, Z., Zhang, Y., Lu, J., Xu, J., Xiao, G., A novel time series forecasting model with deep learning. Neurocomputing, 2019 In press.
[35] T.G. Smith, pmdarima: ARIMA estimators for python, 2017, (
[36] S. Smyl, Forecasting short time series with LSTM neural networks, 2016, (
[37] Snyder, R.D., Ord, J.K., Beaumont, A., Forecasting the intermittent demand for slow-moving inventories: a modelling approach. Int. J. Forecast. 28:2 (2012), 485–496.
[38] Stepnicka, M., Burda, M., Computational intelligence in forecasting (cif) 2016 time series forecasting competition. Proceedings of the IEEE WCCI 2016, IJCNN-13 Advances in Computational Intelligence for Applied Time Series Forecasting (ACIATSF), 2016, IEEE.
[39] A. Suilin, 1st Place Solution of Kaggle Web Traffic Time Series Forecasting, 2017, (
[40] Sutskever, I., Vinyals, O., Le, Q.V., Sequence to sequence learning with neural networks. Proceedings of the Advances in Neural Information Processing Systems, 2014, 3104–3112.
[41] Syntetos, A.A., Babai, M.Z., Gardner, E.S., Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping. J. Bus. Res. 68:8 (2015), 1746–1752.
[42] Villani, M., Kohn, R., Nott, D.J., Generalized smooth finite mixtures. J. Econ. 171:2 (2012), 121–133.
[43] R. Wen, K. Torkkola, B. Narayanaswamy, A multi-Horizon Quantile Recurrent Forecaster, arXiv preprint: 1711.11053 (2017).
[44] Werbos, P.J., Backpropagation through time: what it does and how to do it. Proc. IEEE 78:10 (1990), 1550–1560.
[45] Yu, H.-F., Rao, N., Dhillon, I.S., Temporal regularized matrix factorization for high-dimensional time series prediction. Proceedings of the Advances in Neural Information Processing Systems, 2016, 847–855.
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionSchool of Data Science
Corresponding AuthorKang, Y.
1.Bigo Beijing R&D Center, Bigo Inc., Beijing, 100191, China
2.School of Economics and Management, Beihang University, Beijing, 100191, China
3.IBM China CIC, KIC Technology Center, Shanghai, 200433, China
4.Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen, 518172, China
Recommended Citation
GB/T 7714
Chen, Y.,Kang, Y.,Chen, al. Probabilistic forecasting with temporal convolutional neural network[J]. Neurocomputing,2020.
APA Chen, Y., Kang, Y., Chen, Y., & Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network. Neurocomputing.
MLA Chen, Y.,et al."Probabilistic forecasting with temporal convolutional neural network".Neurocomputing (2020).
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